library(ggplot2)
library(patchwork)
library(dplyr)
library(gridExtra)
library(DT)
library(readxl)
library(cowplot)

library(readr)
library(RColorBrewer)
library(ggrepel)
library(gplots)#
library(pheatmap)
rss <- read.csv("/data/rajewsky/home/skim/Microglia_Heppner/201912_10X_9sets/pyscenic/20220315_pyscenic_preprocessed__rss_cell_type_1st.csv",row.names = 1)

rss_t <- rss %>% t %>% as.data.frame

rss2 <- read.csv("/data/rajewsky/home/skim/Microglia_Heppner/201912_10X_9sets/pyscenic/20220315_pyscenic_preprocessed__rss_cell_type_2nd.csv",row.names = 1)

rss2_t <- rss2 %>% t %>% as.data.frame

rss3 <- read.csv("/data/rajewsky/home/skim/Microglia_Heppner/201912_10X_9sets/pyscenic/20220315_pyscenic_preprocessed__rss_cell_type_3rd.csv",row.names = 1)

rss3_t <- rss3 %>% t %>% as.data.frame

rss4 <- read.csv("/data/rajewsky/home/skim/Microglia_Heppner/201912_10X_9sets/pyscenic/20220315_pyscenic_preprocessed__rss_cell_type_4th.csv",row.names = 1)

rss4_t <- rss4 %>% t %>% as.data.frame

# reg <- read.csv("/data/rajewsky/home/skim/Microglia_Heppner/201912_10X_9sets/pyscenic/reg.csv", stringsAsFactors=FALSE)
# reg[1,1] <- "TF"
# reg[1,2] <- "MotifID"
# colnames(reg) <- reg[1, ]
# reg <- reg[-c(1,2), ]
# reg$AUC <- reg$AUC %>% as.numeric
# reg$NES <- reg$NES %>% as.numeric
# reg$MotifSimilarityQvalue <- reg$MotifSimilarityQvalue %>% as.numeric
# reg$OrthologousIdentity <- reg$OrthologousIdentity %>% as.numeric
# reg$RankAtMax <- reg$RankAtMax %>% as.numeric
# saveRDS(reg, file = "/data/rajewsky/home/skim/Microglia_Heppner/201912_10X_9sets/pyscenic/reg_final.rda")

reg <- readRDS(file = "/data/rajewsky/projects/cdr1as_ko_snRNA/3rd_sequencing_run/pyscenic/reg_final.rda")

1-1. glial cells raw

rss_t_1 <- rss_t[, c("Astrocytes_Ctrl", "Astrocytes_AD", "Astrocytes_ADp40KO",
                       "Microglia_Ctrl", "Microglia_AD", "Microglia_ADp40KO",
                       "MOL_Ctrl","MOL_AD","MOL_ADp40KO",
                       "MFOL_Ctrl","MFOL_AD","MFOL_ADp40KO",
                       "NFOL_Ctrl","NFOL_AD","NFOL_ADp40KO",
                       "OPC_Ctrl","OPC_AD","OPC_ADp40KO")]

mypalette <- brewer.pal(11,"RdYlBu")
morecols <- colorRampPalette(mypalette)

par(mar=c(1,1,1,1))
pheatmap(as.matrix(rss_t_1), cluster_cols = FALSE)

1-2. glial cells z-score

rss_t_z <- as.data.frame(t(apply(rss_t, 1, function(x) (x - mean(x)) / sd(x))))

rss_t_z <- rss_t_z[, c("Astrocytes_Ctrl", "Astrocytes_AD", "Astrocytes_ADp40KO",
                       "Microglia_Ctrl", "Microglia_AD", "Microglia_ADp40KO",
                       "MOL_Ctrl","MOL_AD","MOL_ADp40KO",
                       "MFOL_Ctrl","MFOL_AD","MFOL_ADp40KO",
                       "NFOL_Ctrl","NFOL_AD","NFOL_ADp40KO",
                       "OPC_Ctrl","OPC_AD","OPC_ADp40KO")]

mypalette <- brewer.pal(11,"RdYlBu")
morecols <- colorRampPalette(mypalette)

par(mar=c(1,1,1,1))
pheatmap(as.matrix(rss_t_z), cluster_cols = FALSE)

# heatmap.2(as.matrix(rss_t_z), 
#           trace="none", 
#           col=rev(morecols(50)),
#           #Colv=FALSE,
#           main="cell number per cluster in all samples",
#           scale="row", lhei=c(1.5, 10), lwid = c(2,10), cexCol=0.8)

1-3. neuronal cells raw

rss_t_1 <- rss_t[, c("subiculum_Ctrl", "subiculum_AD", "subiculum_ADp40KO",
                       "CA1_Ctrl", "CA1_AD", "CA1_ADp40KO",
                       "CA2_3_Ctrl","CA2_3_AD","CA2_3_ADp40KO",
                       "Dentate_Gyrus_Ctrl","Dentate_Gyrus_AD","Dentate_Gyrus_ADp40KO",
                       "Inhibitory_Neurons_Ctrl","Inhibitory_Neurons_AD","Inhibitory_Neurons_ADp40KO")]

mypalette <- brewer.pal(11,"RdYlBu")
morecols <- colorRampPalette(mypalette)

par(mar=c(1,1,1,1))
pheatmap(as.matrix(rss_t_1), cluster_cols = FALSE)

1-4. neuronal cells z-score

rss_t_z <- as.data.frame(t(apply(rss_t, 1, function(x) (x - mean(x)) / sd(x))))

rss_t_z <- rss_t_z[, c("subiculum_Ctrl", "subiculum_AD", "subiculum_ADp40KO",
                       "CA1_Ctrl", "CA1_AD", "CA1_ADp40KO",
                       "CA2_3_Ctrl","CA2_3_AD","CA2_3_ADp40KO",
                       "Dentate_Gyrus_Ctrl","Dentate_Gyrus_AD","Dentate_Gyrus_ADp40KO",
                       "Inhibitory_Neurons_Ctrl","Inhibitory_Neurons_AD","Inhibitory_Neurons_ADp40KO")]

mypalette <- brewer.pal(11,"RdYlBu")
morecols <- colorRampPalette(mypalette)

par(mar=c(1,1,1,1))
pheatmap(as.matrix(rss_t_z), cluster_cols = FALSE)

2-1. glial cells replicates raw

rss2_t_1 <- rss2_t[, c("Astrocytes_Ctrl_1", "Astrocytes_Ctrl_4", "Astrocytes_Ctrl_7",
                       "Astrocytes_AD_3", "Astrocytes_AD_6","Astrocytes_AD_9",
                       "Astrocytes_ADp40KO_2","Astrocytes_ADp40KO_5","Astrocytes_ADp40KO_8",
                       "Microglia_Ctrl_1", "Microglia_Ctrl_4", "Microglia_Ctrl_7",
                       "Microglia_AD_3", "Microglia_AD_6","Microglia_AD_9",
                       "Microglia_ADp40KO_2","Microglia_ADp40KO_5","Microglia_ADp40KO_8")]

mypalette <- brewer.pal(11,"RdYlBu")
morecols <- colorRampPalette(mypalette)

par(mar=c(1,1,1,1))
pheatmap(as.matrix(rss2_t_1), cluster_cols = FALSE)

2-2. glial cells replicates z-score

rss2_t_z <- as.data.frame(t(apply(rss2_t, 1, function(x) (x - mean(x)) / sd(x))))

rss2_t_z_1 <- rss2_t_z[, c("Astrocytes_Ctrl_1", "Astrocytes_Ctrl_4", "Astrocytes_Ctrl_7",
                       "Astrocytes_AD_3", "Astrocytes_AD_6","Astrocytes_AD_9",
                       "Astrocytes_ADp40KO_2","Astrocytes_ADp40KO_5","Astrocytes_ADp40KO_8",
                       "Microglia_Ctrl_1", "Microglia_Ctrl_4", "Microglia_Ctrl_7",
                       "Microglia_AD_3", "Microglia_AD_6","Microglia_AD_9",
                       "Microglia_ADp40KO_2","Microglia_ADp40KO_5","Microglia_ADp40KO_8")]

mypalette <- brewer.pal(11,"RdYlBu")
morecols <- colorRampPalette(mypalette)

par(mar=c(1,1,1,1))
pheatmap(as.matrix(rss2_t_z_1), cluster_cols = FALSE)

2-3. Oligo cells replicates raw

rss2_t_1 <- rss2_t[, c("OPC_Ctrl_1", "OPC_Ctrl_4", "OPC_Ctrl_7",
                       "OPC_AD_3", "OPC_AD_6","OPC_AD_9",
                       "OPC_ADp40KO_2","OPC_ADp40KO_5","OPC_ADp40KO_8",
                       "NFOL_Ctrl_1", "NFOL_Ctrl_4", "NFOL_Ctrl_7",
                       "NFOL_AD_3", "NFOL_AD_6","NFOL_AD_9",
                       "NFOL_ADp40KO_2","NFOL_ADp40KO_5","NFOL_ADp40KO_8",
                       "MFOL_Ctrl_1", "MFOL_Ctrl_4", "MFOL_Ctrl_7",
                       "MFOL_AD_3", "MFOL_AD_6","MFOL_AD_9",
                       "MFOL_ADp40KO_2","MFOL_ADp40KO_5","MFOL_ADp40KO_8",
                       "MOL_Ctrl_1", "MOL_Ctrl_4", "MOL_Ctrl_7",
                       "MOL_AD_3", "MOL_AD_6","MOL_AD_9",
                       "MOL_ADp40KO_2","MOL_ADp40KO_5","MOL_ADp40KO_8")]

mypalette <- brewer.pal(11,"RdYlBu")
morecols <- colorRampPalette(mypalette)

par(mar=c(1,1,1,1))
pheatmap(as.matrix(rss2_t_1), cluster_cols = FALSE)

2-4. Oligo cells replicates z-score

rss2_t_z_1 <- rss2_t_z[, c("OPC_Ctrl_1", "OPC_Ctrl_4", "OPC_Ctrl_7",
                       "OPC_AD_3", "OPC_AD_6","OPC_AD_9",
                       "OPC_ADp40KO_2","OPC_ADp40KO_5","OPC_ADp40KO_8",
                       "NFOL_Ctrl_1", "NFOL_Ctrl_4", "NFOL_Ctrl_7",
                       "NFOL_AD_3", "NFOL_AD_6","NFOL_AD_9",
                       "NFOL_ADp40KO_2","NFOL_ADp40KO_5","NFOL_ADp40KO_8",
                       "MFOL_Ctrl_1", "MFOL_Ctrl_4", "MFOL_Ctrl_7",
                       "MFOL_AD_3", "MFOL_AD_6","MFOL_AD_9",
                       "MFOL_ADp40KO_2","MFOL_ADp40KO_5","MFOL_ADp40KO_8",
                       "MOL_Ctrl_1", "MOL_Ctrl_4", "MOL_Ctrl_7",
                       "MOL_AD_3", "MOL_AD_6","MOL_AD_9",
                       "MOL_ADp40KO_2","MOL_ADp40KO_5","MOL_ADp40KO_8")]

mypalette <- brewer.pal(11,"RdYlBu")
morecols <- colorRampPalette(mypalette)

par(mar=c(1,1,1,1))
pheatmap(as.matrix(rss2_t_z_1), cluster_cols = FALSE)

2-5. Neuronal cells replicates raw

rss2_t_1 <- rss2_t[, c("subiculum_Ctrl_1", "subiculum_Ctrl_4", "subiculum_Ctrl_7",
                       "subiculum_AD_3", "subiculum_AD_6","subiculum_AD_9",
                       "subiculum_ADp40KO_2","subiculum_ADp40KO_5","subiculum_ADp40KO_8",
                       "Dentate_Gyrus_Ctrl_1", "Dentate_Gyrus_Ctrl_4", "Dentate_Gyrus_Ctrl_7",
                       "Dentate_Gyrus_AD_3", "Dentate_Gyrus_AD_6","Dentate_Gyrus_AD_9",
                       "Dentate_Gyrus_ADp40KO_2","Dentate_Gyrus_ADp40KO_5","Dentate_Gyrus_ADp40KO_8",
                       "CA1_Ctrl_1", "CA1_Ctrl_4", "CA1_Ctrl_7",
                       "CA1_AD_3", "CA1_AD_6","CA1_AD_9",
                       "CA1_ADp40KO_2","CA1_ADp40KO_5","CA1_ADp40KO_8",
                       "CA2_3_Ctrl_1", "CA2_3_Ctrl_4", "CA2_3_Ctrl_7",
                       "CA2_3_AD_3", "CA2_3_AD_6","CA2_3_AD_9",
                       "CA2_3_ADp40KO_2","CA2_3_ADp40KO_5","CA2_3_ADp40KO_8",
                       "Inhibitory_Neurons_Ctrl_1", "Inhibitory_Neurons_Ctrl_4", "Inhibitory_Neurons_Ctrl_7",
                       "Inhibitory_Neurons_AD_3", "Inhibitory_Neurons_AD_6","Inhibitory_Neurons_AD_9",
                       "Inhibitory_Neurons_ADp40KO_2","Inhibitory_Neurons_ADp40KO_5","Inhibitory_Neurons_ADp40KO_8")]

mypalette <- brewer.pal(11,"RdYlBu")
morecols <- colorRampPalette(mypalette)

par(mar=c(1,1,1,1))
pheatmap(as.matrix(rss2_t_1), cluster_cols = FALSE)

2-6. Neuronal cells replicates z-score

rss2_t_z_1 <- rss2_t_z[, c("subiculum_Ctrl_1", "subiculum_Ctrl_4", "subiculum_Ctrl_7",
                       "subiculum_AD_3", "subiculum_AD_6","subiculum_AD_9",
                       "subiculum_ADp40KO_2","subiculum_ADp40KO_5","subiculum_ADp40KO_8",
                       "Dentate_Gyrus_Ctrl_1", "Dentate_Gyrus_Ctrl_4", "Dentate_Gyrus_Ctrl_7",
                       "Dentate_Gyrus_AD_3", "Dentate_Gyrus_AD_6","Dentate_Gyrus_AD_9",
                       "Dentate_Gyrus_ADp40KO_2","Dentate_Gyrus_ADp40KO_5","Dentate_Gyrus_ADp40KO_8",
                       "CA1_Ctrl_1", "CA1_Ctrl_4", "CA1_Ctrl_7",
                       "CA1_AD_3", "CA1_AD_6","CA1_AD_9",
                       "CA1_ADp40KO_2","CA1_ADp40KO_5","CA1_ADp40KO_8",
                       "CA2_3_Ctrl_1", "CA2_3_Ctrl_4", "CA2_3_Ctrl_7",
                       "CA2_3_AD_3", "CA2_3_AD_6","CA2_3_AD_9",
                       "CA2_3_ADp40KO_2","CA2_3_ADp40KO_5","CA2_3_ADp40KO_8",
                       "Inhibitory_Neurons_Ctrl_1", "Inhibitory_Neurons_Ctrl_4", "Inhibitory_Neurons_Ctrl_7",
                       "Inhibitory_Neurons_AD_3", "Inhibitory_Neurons_AD_6","Inhibitory_Neurons_AD_9",
                       "Inhibitory_Neurons_ADp40KO_2","Inhibitory_Neurons_ADp40KO_5","Inhibitory_Neurons_ADp40KO_8")]

mypalette <- brewer.pal(11,"RdYlBu")
morecols <- colorRampPalette(mypalette)

par(mar=c(1,1,1,1))
pheatmap(as.matrix(rss2_t_z_1), cluster_cols = FALSE)

sessionInfo()
## R version 3.6.0 (2019-04-26)
## Platform: x86_64-redhat-linux-gnu (64-bit)
## Running under: CentOS Linux 7 (Core)
## 
## Matrix products: default
## BLAS/LAPACK: /usr/lib64/R/lib/libRblas.so
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
##  [1] pheatmap_1.0.12    gplots_3.1.1       ggrepel_0.9.1      RColorBrewer_1.1-2
##  [5] readr_2.1.2        cowplot_1.1.1      readxl_1.3.1       DT_0.18           
##  [9] gridExtra_2.3      dplyr_1.0.7        patchwork_1.1.1    ggplot2_3.3.5     
## 
## loaded via a namespace (and not attached):
##  [1] gtools_3.9.2       tidyselect_1.1.1   xfun_0.29          bslib_0.2.5.1     
##  [5] purrr_0.3.4        colorspace_2.0-2   vctrs_0.3.8        generics_0.1.1    
##  [9] htmltools_0.5.2    yaml_2.2.1         utf8_1.2.2         rlang_0.4.12      
## [13] jquerylib_0.1.4    pillar_1.6.4       glue_1.6.1         withr_2.4.3       
## [17] DBI_1.1.1          lifecycle_1.0.1    stringr_1.4.0      munsell_0.5.0     
## [21] gtable_0.3.0       cellranger_1.1.0   caTools_1.18.2     htmlwidgets_1.5.4 
## [25] evaluate_0.14      knitr_1.37         tzdb_0.2.0         fastmap_1.1.0     
## [29] fansi_1.0.2        highr_0.9          Rcpp_1.0.8         KernSmooth_2.23-15
## [33] scales_1.1.1       jsonlite_1.7.3     hms_1.1.1          digest_0.6.29     
## [37] stringi_1.7.6      grid_3.6.0         bitops_1.0-7       tools_3.6.0       
## [41] magrittr_2.0.1     sass_0.4.0         tibble_3.1.6       crayon_1.4.2      
## [45] pkgconfig_2.0.3    ellipsis_0.3.2     assertthat_0.2.1   rmarkdown_2.11    
## [49] R6_2.5.1           compiler_3.6.0